Bibliographic record
Abstract
In a traditional cellular network the base stations (BSs) are deployed regularly according to a pre-determined pattern modeled by hexagons. The deployment of a larger number of BSs improves the network performance. However, a dense regular deployment of BSs is prohibitively expensive and in most cases not feasible. Alternatively the network can grow in an organic fashion by the deployment of BSs according to the traffic demand. A high traffic demand in a given locality is reflected in a higher number of BSs deployed in the area. In this paper, we propose a practical framework for the resource allocation of cellular networks with an irregular BS deployment pattern. To this end, a network clustering technique is proposed which forms clusters of coordinating BSs. The coordinated resource allocation among the BSs within each cluster is devised to achieve proportional fairness. The performance of the proposed framework is evaluated with regular as well as irregular deployment of BSs. The results are compared against standard resource allocation techniques and show promising results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".